method for determining a flow rate of users and / or polluting emissions on at least one strand of a network

Abstract

The invention relates to a method for determining the number of uses of each mode of transport on a strand of a transport network in a predetermined space, for which:a) spatial and temporal learning data (DSP) are acquired (Acq),b) the predetermined space is discretized (Z) into zones (ZD / ZA);c) for each journey defined by an origin zone (ZD) and a destination zone (ZA), c1) learning trajectories (Traj) are determined (For); c2) we determine (MDT), for each learning trajectory (Traj), the path taken on the transport network and the mode of transport;d) from the results of step c2), we create (Real) a model (Mod) of the number of uses of each mode of transport on the strand;e) we acquire a data matrix (Mat) and we apply it (App) to the model (Mod) to determine the number of uses (Nb_mdt) of each mode of transport on the strand. Figure 1 to be published

Description

Title of the invention: method for determining a flow rate of users and / or polluting emissions on at least one strand of a network Technical field

[0001] The present invention relates to the field of determining traffic of vehicles of different types or users for a transport network, when different modes of transport are possible. In particular, the invention relates to a method for determining numbers of uses (or users) of different modes of transport on at least one strand of a transport network. Thus, the flow rate of each type of mode of transport (or the rate of use or the frequency of use of each mode of transport) can be determined.

[0002] The invention also relates to the field of determining pollutant emissions emitted on at least one section of the road network, taking into account different modes of transport.

[0003] Today, metropolises and road managers have travel modeling tools to plan and simulate the impact of regulatory measures and future works, in order to reduce congestion or improve air quality. However, these models are very difficult to implement (need for very expensive population survey data, need for a significant calibration effort), difficult to maintain (maintenance is delegated to design offices, so it is difficult to develop the tool quickly), and difficult to update (population surveys are conducted every 5 to 10 years).

[0004] There is therefore a strong need for tools for determining vehicle flow rates within a transport network without having to use input data that is too costly and difficult to obtain, such as mobility surveys, which are also static and are not suitable for predicting rapid changes in mobility, or real-life usage data, such as "floating car data" (FCD, which can be translated as data on journeys made and measured), which are dynamic but not necessarily available across all sections of a transport network. Furthermore, these alternative tools should be easy to use by non-experts, quick to execute to easily evaluate and compare several case studies, and finally reliable by reproducing recent counting data on the road network in question as best as possible.

[0005] Furthermore, according to the World Health Organization (WHO), approximately 18,000 deaths per day are attributable to poor air quality, which raises the estimate to approximately 6.5 million deaths per year. Air pollution also represents a significant financial challenge: a Senate inquiry committee estimated that the total cost of air pollution was between 68 and 97 billion euros per year for France, in an assessment released in July 2015, which included both the health damage caused by pollution and its consequences for buildings, ecosystems, and agriculture. The transport sector still represents one of the largest sources of pollutants, despite the numerous measures implemented by public authorities and technological advances in the field. Transport, all modes combined, is responsible for approximately 50% of global nitrogen oxide (NOX) emissions and approximately 10% of PM2.5 particulate emissions (i.e., particles with a diameter of less than 2.5 microns).Road transport alone accounts for a considerable share of this transport-related contribution, with 58% of NOX emissions and 73% of PM2.5 particulate emissions. These emissions are mainly due to three factors: tailpipe emissions, abrasion emissions and evaporative emissions. While heavy goods vehicles are the main pollutant emitters, it is private vehicles, more represented in densely populated urban areas, which have the highest impact on citizens' exposure to poor air quality.

[0006] Measures implemented at the local level to manage transport use (such as better transport planning and measures to encourage modal shift, i.e. changing transport modes), as well as the gradual renewal of the vehicle fleet, have helped to limit exhaust gas emissions from road transport in cities and urban areas. Indeed, worldwide, road transport activity has increased by a quarter over the last decade, while NOX emissions have increased by 5% and particulate emissions have decreased by 6%. Despite these improvements, pollution levels still exceed the thresholds set by the WHO in many cities.

[0007] To significantly improve air quality in their territory, French urban communities must take action to reduce transport-related emissions. For example, currently, the road sector represents, in the Lyon Metropolitan Area, two-thirds of total nitrogen oxide (NOX) emissions and one-third of total PM10 particle emissions (i.e. particles with a diameter of less than 10 microns). However, to date, the teams of the Urban Mobility Roads department of this community do not have tools enabling them to determine the impact on air quality of the development of the various transport networks and / or the characteristics of the vehicles circulating on the transport networks. Decision-making in relation to these subjects therefore does not take into account the impact on polluting emissions, due to a lack of tools.

[0008] Consequently, it is difficult for cities to make the right decisions regarding the development of transport network infrastructure and legislation, for example regarding the characteristics of vehicles authorized to circulate on the networks, without having at their disposal precise tools for evaluating and projecting the impact of the measures envisaged on the polluting emissions of the different modes of transport and air quality. These new tools should ideally make it possible to evaluate the impact of the measures on very fine temporal and spatial scales (of the order of one minute, and of the order of ten meters) taking into account the different means of transport, the different types of vehicles and the journeys made by the different users of these networks. Prior art

[0009] The mobile phone has become one of the essential objects of everyday life for many humans. The mobile phone is designed to be connected to the mobile telephone network, but more recent technologies allow it to also be connected and detected by other devices, such as satellites via the Global Positioning System (GPS) protocol, wireless network terminals via the WIFI protocol or other mobile phones via the Bluetooth protocol. This connection data can make it possible to determine more or less precisely the position of the mobile device, and therefore a priori that of its owner (hereinafter called the user).

[0010] It is known, notably from the publication by Loïc Bonnetain, Angelo Fumo, Nour-Eddin El Faouzi, Marco Fiore, Razvan Stanica, Zbigniew Smoreda, Cezary Ziemlicki; 2021. “TRANSIT: Fine-grained human mobility trajectory inference at scale with mobile network signaling data” - Transportation Research Part C: Emerging Technologies, to use NSD type data (for “Network Signal Data” in English, which means “network signal data”), to illustrate travel on major axes of a transport network of a large city. But this method does not make it possible to determine the number of users of each type of transport mode, and even less to determine polluting emissions, on a strand of the transport network.

[0011] We also know the document by Manon Seppecher, 2022, “Exploration of mobile telephony data for the reconstruction of global patterns of urban mobility for the calculation of large-scale emissions, Thesis manuscript”, which describes a method seeking, after analysis of user trajectories, to estimate the total distance traveled by users over regions of a territory on the one hand, and to estimate the average speed of users over the same regions on the other hand. Then, emissions per region are estimated with a COPERT model for “COmputer Program to calculate Emission from Road Transport”, in English, which means “Computer program to calculate emissions from road transport”. However, this method does not allow the identification of the mode of transport. Therefore, this method does not allow discretization on each strand of the network, the share coming from each mode of transport and therefore, it does not apply an emission model dependent on the identified mode of transport.

[0012] Patent application CN105426636 uses a traffic model to obtain flow rates on the strands of a road network and estimates pollutant emissions from a COPERT model. This application does not identify the mode of transport associated with each trajectory.

[0013] Patent application CN112767686 uses GPS data for "Global Positioning System". However, the use of GPS data is less representative of the population than spatial and temporal data from the telephone to a mobile telephone network because this data is only possible when the user uses the GPS of his telephone. In addition, each trajectory is considered individually, which imposes significant calculation times and computer memory capacities.

[0014] Patent application CN108682156 also uses taxi GPS data, which limits the type of transport mode used in this method. It also uses GIS (acronym for Geographic Information System) data, but it does not allow the number of uses of each type of transport mode on a strand of the network to be determined, nor does it allow the pollutant emissions to be precisely determined. Summary of the invention

[0015] The technical problem of the invention consists in designing a method for determining the traffic flows on a section of the road network within a predetermined space, i.e. the number of uses of each type of transport mode (or the number of users of each transport mode), or the flow rate of each type of transport mode, or the frequency of use of each type of transport mode, on the section considered.

[0016] It is preferable to seek to minimize the calculation time and the computer memory and / or the number of processors required.

[0017] Furthermore, the method may also seek to enable the rapid evaluation, in terms of user flow and / or pollutant emissions, of modifications to be made to the transport network (infrastructure modifications, such as the addition of a road lane, speed limitation, the addition of a traffic light or a roundabout, the development of a soft mobility route, the addition of bus lines or tram or metro etc.) or decisions to restrict certain vehicles to certain areas to limit congestion and polluting emissions.

[0018] Furthermore, the invention seeks to use data based on a high population penetration rate (i.e. a very large proportion of mobile phone users), even if this data is of low resolution.

[0019] The invention relates to a method for determining the number of uses of different modes of transport on at least one strand of a transport network within a predetermined space, by means of telephones and a mobile telephone network to which said telephones can connect, and by means of at least one transport network comprising strands. In addition, at least the following steps are carried out by computer means, such as a computer: (a) acquiring spatial and temporal learning data on the connection of telephones to said mobile telephone network, the spatial and temporal learning data comprising the positions of the antennas of the mobile telephone network to which each telephone has connected and the times at which these connections of each telephone to the antennas took place, preferably the spatial and temporal learning data being CDR or NSD data b) the predetermined space is discretized into zones; (c) for each path defined by an origin zone and a destination zone, each origin zone and each destination zone being among the said zones of the predetermined discretized space, cl) learning trajectories of said path are determined from the acquired spatial and temporal learning data, each learning trajectory corresponding to the succession of positions of the antennas to which one of said telephones has connected and the times of these connections; c2) for each determined learning trajectory, the path taken on the transport network and the associated mode of transport are determined, the path taken being a succession of strands of the transport network at times of passage; d) from the paths and modes of transport determined in step c2), a model is produced of the number of uses of each mode of transport on said at least one strand of the transport network linking the trajectories to a number of uses of each mode of transport on said at least one strand of the transport network; e) acquiring a data matrix and applying said data matrix to said model of the number of uses for each mode of transport on said at least one strand of the transport network to determine the number of uses of each mode of transport on said at least one strand of the transport network from said data matrix.

[0020] Advantageously, in step d), to produce the model of the number of uses of

[0021]

[0022]

[0023]

[0024] each mode of transport on said at least one strand of the transport network, the following sub-steps are carried out: dl) for each journey, we identify a first number of learning trajectories linked to each mode of transport; d2) for each journey, a second number of learning trajectories linked to each mode of transport and passing through said at least one strand of the transport network is identified; d3) then for each journey, the proportion of learning trajectories linked to each mode of transport and passing through said at least one strand of the transport network is determined, this proportion being, for each mode of transport, the ratio between the second number and the first number; d4) the model of the number of uses of each mode of transport on said at least one strand of the transport network is produced from the following equation: f — V ynf with f the number of uses of the mode of J ABM J predetermined transport M passing through said at least one strand bi of the transport network the proportion calculated in step d3) for each journey from the origin zone A to the destination zone B of the predetermined transport mode M passing through said at least one strand bi of the transport network abm 'C number of trajectories of the journey from the origin zone A to the destination zone B for the predetermined mode of transport F being the space of the transport network considered. Preferably, pre-processing and / or filtering of the spatial and temporal learning data is performed. Advantageously, a first correction coefficient is assigned to said learning trajectories as a function of the users of the telephones for which the spatial and temporal learning data were acquired in step a). Preferably, second spatial and temporal data of connection of telephones to said mobile telephone network are acquired, the second spatial and temporal data comprising the positions of the antennas of the mobile telephone network to which each telephone has connected and the times at which these connections of each telephone to the antennas took place, and second trajectories are determined so as to form said data matrix acquired from the second spatial and temporal data and preferably a second rectification coefficient is assigned to said second trajectories as a function of the users of the telephones for which the second spatial and temporal data have been acquired. According to a configuration of the invention, the second trajectories are grouped into clusters of second trajectories, preferably by means of a re agglomerative grouping based on a spatio-temporal distance, said spatio-temporal distance being a weighted sum of a spatial distance between two second trajectories and a temporal distance depending on the difference in duration between the two second trajectories, said spatial distance and the temporal distance being determined from the second spatial and temporal data, the groupings differentiating the speeds and the trajectories.

[0025] Preferably, in step cl), different learning trajectories determined are grouped into clusters of learning trajectories.

[0026] Advantageously, the learning trajectories are grouped together using an agglomerative grouping method based on a spatio-temporal distance, said spatio-temporal distance being a weighted sum of a spatial distance between two learning trajectories and a temporal distance depending on the difference in duration between the two learning trajectories), said spatial distance and the temporal distance being determined from the spatial and temporal learning data, the groupings differentiating the speeds and the trajectories.

[0027] According to one aspect of the invention, in steps c1) and c2), at least the following sub-steps are carried out: - transport graphs are constructed for each mode of transport, each transport graph comprising nodes, road sections connecting the different nodes and the average travel speeds on each road section, each road section of the transport graph representing the possible routes by the associated mode of transport, the superposition of the transport graphs forming the transport network, each strand of the transport network corresponding to one of said road sections of at least one of said transport graphs, and at least the following steps are implemented: i) from the acquired spatial and temporal learning data, a succession of location-time pairs defined by locations corresponding to said positions of the antennas from the acquired spatial and temporal learning data and by the times corresponding to these locations is determined and at least one sub-route connecting two successive location-time pairs is formed, a succession of sub-routes determining a learning trajectory; ii) For each transport graph, the successive positions of the telephone on successive identified nodes of the transport graph are determined from the location-time pairs, the successive identified nodes being positioned as close as possible to each location; iii) Then for each transport graph and for each sub-route, we determine, by shortest path optimizations, a predetermined number of paths possible allowing to connect the different successive identified nodes by road sections, each possible path comprising the crossing nodes connecting all the road sections of the possible path, and the time of passage of each crossing node is calculated from the average speeds associated with each road section, each possible path being determined by the list of all the pairs of crossing nodes - times of passage of said possible path; iv) a correlation index is determined between each of the determined possible paths and the learning trajectory, the correlation index being representative of the spatial proximity and / or the temporal proximity of each successive identified node with the locations of the learning trajectory; and (v) the mode of transport of each learning trajectory is determined by determining the mode of transport which optimizes the correlation index of the learning trajectory, and the path traveled corresponding to the possible path optimizing the correlation index.

[0028] Advantageously, a sub-graph is extracted from each transport graph, the sub-graph being a part of the transport graph limited to a predetermined width around each sub-route determined in step i) and the sub-graph is used instead of the transport graph for each of steps ii) to v).

[0029] According to a variant of the invention, the number of uses of each mode of transport passing through at least one strand of the transport network, preferably through at least one group of strands of the transport network and more preferably through all the strands of the transport network, is displayed on a map representing the transport network.

[0030] The invention also relates to a method for determining polluting emissions on at least one strand of the transport network within a predetermined space, in which the method described above is implemented and the polluting emissions due to each mode of transport of each learning trajectory and / or of the data matrix passing through said strand are determined for the at least one strand of the transport network.

[0031] According to one embodiment of the invention, the polluting emissions of each mode of transport are determined by multiplying a polluting emissions value of each mode of transport on said at least one strand of the transport network and the number of uses of each associated mode of transport on said at least one strand of the transport network.

[0032] According to a variant of the invention, the determined pollutant emissions are displayed on a map representing the transport network.

[0033] Preferably, a fleet of vehicles is applied to determine the polluting emissions, said fleet of vehicles identifying a distribution of different types of vehicles and a pollutant emissions value depending on the different types of vehicles.

[0034] The invention also relates to a method for managing the infrastructure of a transport network within a predetermined space, in which at least the following steps are implemented: 1) The number of uses and / or the pollutant emissions of each mode of transport for at least one strand of the transport network are determined by means of the method for determining the number of uses of each mode of transport on at least one strand of the transport network within the predetermined space according to one of the variants or combinations of variants described above or the method for determining the pollutant emissions according to one of the variants or combinations of variants described above and 2) At least one infrastructure of the transport network is modified according to the number of uses of each mode of transport or pollutant emissions, preferably an infrastructure for which the number of uses is greater than a predetermined threshold. List of figures

[0035] Other characteristics and advantages of the methods according to the invention will appear on reading the following description of non-limiting examples of embodiments, with reference to the appended figures described below. [Fig 1]

[0036] [Fig. 1] represents a first embodiment of the method for determining the number of uses of different modes of transport on at least one strand of a transport network within a predetermined space according to the invention. [Fig 2]

[0037] [Fig.2] represents an exemplary embodiment of the model of the number of uses of each mode of transport on said at least one strand of the transport network of the method for determining the number of uses of different modes of transport on at least one strand of a transport network within a predetermined space according to the invention. [Fig 3]

[0038] [Fig. 3] represents a second embodiment of the method for determining the number of uses of different modes of transport on at least one strand of a transport network within a predetermined space according to the invention. [Fig 4]

[0039] [Fig.4] represents a third embodiment of the method for determining the number of uses of different modes of transport on at least one strand of a transport network within a predetermined space according to the invention. [Fig 5]

[0040] [Fig.5] represents a fourth embodiment of the method for determining the number of uses of different modes of transport on at least one strand of a transport network within a predetermined space according to the invention. [Fig 6]

[0041] [Fig.6] represents an example of a step of identifying the mode of transport and the path of the method of determining the number of uses of different modes of transport on at least one strand of a transport network within a predetermined space according to the invention. [Fig 7]

[0042] [Fig.7] represents an example of application of the method for determining the number of uses of different modes of transport on at least one strand of a transport network within a metropolis according to the invention. Description of the embodiments

[0043] The invention relates to a method for determining the number of uses of different modes of transport on at least one strand of a transport network within a predetermined space. The predetermined space may be, for example, an urban agglomeration, such as a metropolis, a region or a country. The transport network then considers all the roads, transport lines of the different modes of transport connecting points within the predetermined space.

[0044] Alternatively, the number of uses (or users) of different modes of transport may be the user throughput, i.e. the number of users over a predetermined period (e.g. an hour, a day or a month), also called the frequency, or the rate of use of each mode of transport, the rate giving the proportion of the use (or number of users) of each mode of transport. Subsequently, the number of uses may be replaced by "the number of users", or "the user throughput", or "the number of passages" or "the frequency of uses" or "the rate of use".

[0045] The different modes of transport may for example include bicycle, scooter, walking, car, heavy goods vehicles, motorcycle, bus, tram, metro, and / or train.

[0046] Thus, the method makes it possible to determine, on a particular strand of the transport network, the number of passages (a passage being linked to a user and therefore to a use) in connection with each mode of transport. When we are interested in a number of passages over a predetermined duration, we can thus know the variability of the number of passages and therefore of the congestion. The method according to the invention can in particular make it possible to determine the number of uses of different modes of transport over different periods, for example during the week or at the weekend, during peak periods or outside peak periods.

[0047] This method can in particular be used to modify the infrastructure of the transport network (adding lanes, building specific lanes for particular modes of transport, bicycle or scooter for example), to modify the speed limits of certain sections (at least one section) of the transport network or to prohibit the circulation of certain vehicles on certain sections (at least one section) of the transport network by evaluating the impact of these modifications on traffic congestion or on air quality.

[0048] The method according to the invention uses mobile phones (hereinafter referred to as phones) and a mobile telephone network to which the mobile phones can connect, as well as at least one transport network comprising strands interconnected to each other. The transport network combines different means of transport and on the same strand of the transport network, different means of transport (bus, tram, car, motorcycle, truck, bicycle, walking for example) may be possible.

[0049] In addition, at least the following steps are carried out: (a) acquisition of spatial and temporal data for learning the connection of telephones to the mobile telephone network;

[0050] b) discretization of the predetermined space into zones;

[0051] c) determining the learning trajectories for each journey defined by an origin zone (also called “departure zone”) and a destination zone (also called “arrival zone”), and determining the path and mode of transport of each learning trajectory

[0052] d) producing a model of the number of uses of each mode of transport on the strand of the transport network;

[0053] e) acquisition of a data matrix (also called a "trip matrix") and application of the data matrix to the model of the number of uses for each mode of transport on the strand of the transport network.

[0054] At least some of the preceding steps (steps b), c), d) and / or the part of e) where the model is applied for example) can be implemented by computer means, such as a computer. Preferably, all the steps are implemented by computer means.

[0055] [Fig.l] illustrates, in a schematic and non-limiting manner, a first embodiment of the method for determining the number of uses of different modes of transport on at least one strand of a transport network within a predetermined space according to the invention.

[0056] In this figure, spatial and temporal DSP training data are acquired Acq from Tel phones (mobiles, also called portables) and a mobile telephone network Res.

[0057] The predetermined space Z is discretized into zones (which will serve as origin zones ZD and destination zones ZA in the following step) from a transport network RT in the predetermined space and possibly from the spatial and temporal learning data DSP.

[0058] In the figure, the dotted line elements represent the elements that are optional.

[0059] From the spatial and temporal learning data DSP and paths defined by an origin zone ZD and a destination zone ZA among the zones resulting from the discretization Z of the predetermined space, learning trajectories Traj are determined.

[0060] We then determine MDT an output data Ch, the output data Ch comprising the mode of transport and the path of each learning trajectory Traj.

[0061] We can then carry out step d) of the method, i.e. the construction Real of a model Mod for determining the number of uses of each mode of transport on a strand of the transport network from the output data Ch of the different learning trajectories Traj.

[0062] We can then apply the Mod model to a data matrix Mat to determine the number of uses Nb_mdt of each mode of transport on the transport network strand.

[0063] Optionally, the pollutant emissions Pol can then be calculated on the transport network strand from the number of uses Nb_mdt of each mode of transport on the transport network strand.

[0064] Of course, all the steps described can be carried out on several strands of the transport network, or even on all the strands of the transport network.

[0065] [Fig. 3] illustrates, in a schematic and non-limiting manner, a second embodiment of the method for determining the number of uses of different modes of transport on at least one strand of a transport network within a predetermined space according to the invention.

[0066] In this figure, the elements and references identical to [Fig.l] correspond to the same elements and references as [Fig.l] and are therefore not re-detailed.

[0067] In this embodiment, before determining For the learning trajectories Traj directly from the raw (i.e. directly acquired) spatial and temporal DSP learning data, a pre-processing step Pre and / or a Filtering step Fil can be carried out. The pre-processing step Pre can be carried out before the Fil filtering step as shown in [Fig.3], or in reverse order.

[0068] When using the spatial and temporal training data DSP to discretize Z the predetermined space into zones, used as origin zones ZD and destination zones ZA, these data are preferably used after the preprocessing steps Pre and / or filtering Fil.

[0069] Of course, all the steps described can be carried out on several strands of the transport network, or even on all the strands of the transport network.

[0070] Step a): acquisition of spatial and temporal data for learning the connection of telephones to the mobile telephone network

[0071] During this step, spatial and temporal data for learning the connection of telephones to the mobile telephone network are acquired (or measured or recorded), these spatial and temporal data being constituted by events recorded by the mobile telephone network (by the mobile telephone operators) detecting a connection between the telephone and the mobile telephone network (to an antenna of this network). The spatial and temporal learning data include the positions of the antennas of the mobile telephone network to which each telephone connects and the times at which these connections of each telephone to the antennas take place.

[0072] Preferably, the spatial and temporal training data may be NSD data for "Network Signal Data" or CDR data for "Call Detail Records".

[0073] NSD data is generated as soon as a phone connects to an antenna to find a network, for whatever reason: therefore, NSD data is recorded almost permanently from the moment the phone is not in airplane mode.

[0074] CDR data is a subset of NSD data. CDR data is that which allows the user to be billed based on his use of the telephone via the mobile telephone network. Therefore, the CDR data preferably includes exclusively telephone transaction recording data consisting of an identifier making it possible to recognize the user's telephone, the type of connection event (call, SMS and / or Internet exchange data), the time of the connection event (possibly the start time and the end time of the event) as well as the position of the antenna to which the telephone was connected at the time when the event took place (therefore the position of the antenna through which the event passed).Compared to NSD data, CDR data is very weak spatial and temporal data because it only considers events necessary for billing by operators while the data. NSD includes other additional information.

[0075] Being qualitatively very low in terms of spatial precision of the telephone and temporal frequency, these CDR data are generally excluded from methods for determining user trajectories and a fortiori for determining modes of transport or paths on these trajectories.

[0076] Nevertheless, these CDR and NSD data offer a very interesting population penetration rate, in particular compared to the GPS data generally used, that is to say that they make it possible to recover the data of a greater number of users since today the majority of the population has a mobile phone and no particular application needs to be used by the user to access these data.

[0077] Advantageously, pre-processing and / or filtering of the spatial and temporal learning data can be carried out. The filtering can, for example, comprise the removal of aberrant movements (which can also be called "outliers"). For this filtering, aberrant movements are removed from the spatial and temporal learning data acquired in step 1, by means of filtering the aberrant movements. An aberrant movement is, for example, a movement for which there have been errors in measuring the time-stamped positioning data, or which includes aberrant portions of movement: in particular large detours, loops, etc. This step makes it possible to facilitate spatio-temporal grouping.

[0078] We call displacement the succession of antenna positions, over time, of the same telephone during these connections to the network. The displacements are therefore derived from the spatial and temporal learning data.

[0079] According to one embodiment of the invention, aberrant movements can be identified by a data partitioning method based on a distance between the spatial and temporal learning data, in particular the DBSCAN method (from the English "density-based spatial clustering of applications with noise"), the aberrant movements being those which do not belong to a grouping formed by the data partitioning method. Other similar methods can be implemented for this type of filtering.

[0080] The DBSCAN method makes it possible to group sets of movements into clusters in a hyperspace according to the following rules: • For a new displacement Xt which is not assigned to any cluster, we look to see if there are displacements belonging to a cluster already identified within a distance e of X, • If yes, Xj belongs to the closest cluster as well as all the de- placements at a distance less than s from Xj • If not, we look at how many trips there are located at a distance less than £ from Xj • If there are more than nmn, a new cluster is created and all movements at a distance less than e from Xi are assigned to the new cluster, • If there are fewer than nmû^ Xj is assigned to the group of aberrant displacements and can still be assigned to a cluster as long as displacements located at a distance less than £ from X{ are neither assigned to a cluster nor to the group of aberrant displacements.

[0081] The hyperparameters for tuning a DBSCAN data partitioning method are therefore e, which defines the minimum inter-cluster distance, nmm which defines the minimum number of samples present in a cluster and the distance measure used. In a simplified manner, if we decrease the value of s, we increase the number of clusters and the number of outlier displacements, if we decrease the value of nmm^ we increase the number of clusters and we decrease the number of outlier displacements. The DBSCAN method is interesting because it does not require an a priori number of clusters to be found, and it allows to group everything that really resembles each other into clusters, as long as there is a certain density in the cloud of displacements, and to separate what does not resemble anything else into outlier displacements.

[0082] According to an embodiment option of the invention, in order to find the optimal values ​​of the parameters of the DBSCAN method, one can opt for the silhouette score. The latter makes it possible to ensure cohesion of the movements within each cluster and separation from the other clusters. The values ​​of the silhouette score vary from -1 to 1. A value close to 1 indicates that the movements are closer to the movements of their cluster than to the movements forming the neighboring clusters. Conversely, a value close to -1 can reveal that certain movements have been attributed to the wrong cluster. The silhouette score is calculated from the average intra-cluster distance (a) and the average distance between the closest clusters (b). It is expressed by:

[0083] Silhouette Score = — inax (add?)

[0084] Such that, a is the average distance between each movement within a cluster and b is the distance between a movement of a cluster and the nearest cluster of which the movement is not part. In summary, the silhouette score provides a quantitative assessment of the quality of the clustering and allows to have clusters having good internal cohesion and separation from other clusters.

[0085] Then, the evaluation of the two parameters £ and >lmm can be done in two steps. First, we can find the optimal value of s which corresponds to the best silhouette score by temporarily fixing a value of nmm of the same order of magnitude as what we expect to have. The value s found is used to calibrate in order to obtain the highest possible silhouette score.

[0086] According to an implementation of the invention, to efficiently group movements according to their spatial similarity and consequently identify aberrant movements, one can use the Fréchet distance which gives a low distance to two movements which are very close to each other throughout the entire journey, and a high distance if the movements move away from each other, even if they are very close during almost the entire journey. This distance is often illustrated as the minimum leash distance necessary for a master walking his dog where one movement is the master's path and the other movement is the dog's path.

[0087] As an example, to operate the DBSCAN algorithm, the Fréchet distance between each pair of displacements of the plurality of displacements (possibly preprocessed and possibly selected) can be calculated. The Fréchet distance can be calculated using dynamic programming or can be approximated by a neural network (as described for example in the patent application whose filing number is: FR 2307300).

[0088] The pre-processing may for example comprise steps to limit noise, and / or to improve the accuracy of learning. This step is particularly useful for improving the accuracy of the results of the method and of the model produced in step d).

[0089] Thanks to pre-processing, the homogeneity of the different learning trajectories can be improved. To do this, the pre-processing can consist of forming a vector having a predetermined number of points of the learning trajectories and identical for all the learning trajectories, so as to limit the computation time and the computing resources (memory and processors) required. This pre-processing is particularly advantageous when grouping the learning trajectories into a cluster. The pre-processing of the learning trajectories can then be carried out

[0090] For pre-processing, it is possible, for example, to convert the spatial and temporal learning data (corresponding to each telephone) into a vector having a predetermined number of points. In other words, the spatial and temporal learning data of each telephone are converted into a vector with an identical number of points for all the movements considered. This pre-processing makes it possible, thanks to this homogeneity, to limit the calculation time as well as the computing resources (memory and processor) required. Indeed, each de placement depending on its duration and measurement mode can have a different number of spatial and temporal learning data.

[0091] According to one embodiment, the number of points of the displacement (after preprocessing) can be between 5 and 100, and preferably between 10 and 50. Thus, a good compromise is obtained between precision of the representation of the displacements, calculation time and necessary computer resources.

[0092] For a movement, we can note the acquired spatial and temporal learning data in the following manner: trace := Vj y fj ) y WHERE trace is the vector of a displacement, K the number of spatial and temporal data for learning the movement, x; the longitude of point i, y; the latitude of point i, and t; the time of point i.

[0093] According to an exemplary implementation, the preprocessing may consist of an interpolation of the spatial and temporal learning data on a preprocessing time vector of predefined length (number of displacement points), noted pj, ..., with N the number of displacement points, where the instants are distributed linearly between li and such that:

[0094] =

[0095] tK = ~tN

[0096] f li+] '1 jV-1

[0097] Then, we can implement the interpolation by forming the new traceinterpolated vector for each displacement in the following way:

[0098] traceinîerpoieé := [ ( ■ ?i ), • • ■, ( Av, tN ) ]

[0099] Where / yy, ) is an interpolation of the longitude and latitude of the vector lrace at \ 7 jy the instant t,. The interpolation for this preprocessing can be a linear interpolation between the two points of the acquired spatial and temporal learning data which surround the instant considered during the preprocessing. As an example, for an instant 4 of the preprocessing time vector between the instants t; and ti+i of acquisition, we can write respectively for the longitude and for the latitude:

[0100] Y- _ Xi ( ) +¾ i ( )

[0101] ~ _ y(LA)+>Ai(CrM to shoot

[0102] Step b): discretization of the predetermined space into zones

[0103] In this step, the predetermined space is discretized into zones. These zones will be used, in the following steps, as origin and destination zones in the transport network. For example, the transport network may comprise the entire French transport network and the zones of the predetermined space may be the different departments or the different regions; alternatively, the transport network can include the transport network of a certain area (a certain predetermined space), for example File de France (all transport combined), and we can identify zones such as residential zones, commercial or industrial zones to determine the daily movements of users from their places of residence to their places of work (or vice versa).

[0104] The zones can also be discretized by using spatial and temporal learning data. Indeed, it is possible to identify zones where the telephone has remained stationary or within a restricted perimeter (within a radius of 200m for example) for a certain time (half an hour or an hour for example) as zones (of origin and / or destination). Using CDR data for this identification is interesting because this data makes it possible to reach a larger population and therefore to have a broader spectrum to identify the zones of the predetermined space.

[0105] Determining the zones is particularly useful when grouping the learning trajectories into clusters since the learning trajectories can then be grouped according to the zones (in particular into origin and / or destination zones).

[0106] The zones also make it easier to create the model produced in step d).

[0107] To discretize the predetermined space, one can for example create regular zones, that is to say divide the predetermined space into squares with predefined sides. Alternatively, one can use standard zones (a district, a city or a commune, a department, for example) or use IRIS zones meaning "Grouped Islands for Statistical Information".

[0108] Step c): determination of the learning trajectories for each journey defined by an origin zone and a destination zone, and determination of the path and mode of transport of each learning trajectory

[0109] Step c) comprises two sub-steps cl) and c2) carried out for each path, a path being defined by an origin zone and a destination zone, the path going from the origin zone to the destination zone (in other words, the path is oriented). The origin and destination zones are chosen from the zones resulting from the discretization of the predetermined space.

[0110] Thus, for each path defined by an origin zone and a destination zone: cl) learning trajectories of the path are determined, from the acquired spatial and temporal learning data, each learning trajectory corresponding to the succession of positions of the antennas to which one of the telephones has connected and the times of these connections. Thus, for a path, several learning trajectories are identified linking the origin zone to the destination zone of the path concerned. These different trajectories can be linked to different modes of transport or to different possible paths for the same mode of transport. c2) then, for each determined learning trajectory, the path traveled on the transport network and the associated mode of transport are determined, the path traveled being a succession of strands of the transport network at times of passage. Indeed, depending for example on the average transport speed, different modes of transport can be distinguished: for example, the speed of walking (approximately 5 km / h) is lower than that of the bicycle or scooter, itself lower than that of the bus or tram, itself lower than that of the car. A mode of transport can also be identified when a strand of the network directly identifies a strand of a specific mode of transport (for example the metro). The method for identifying the mode of transport and the path traveled defined in the applicant's patent application FR 3130488 A1 can also be implemented for each learning trajectory.

[0111] Advantageously, a first correction coefficient can be assigned to the learning trajectories according to the users of the telephones for which the spatial and temporal learning data were acquired in step a). For example, the spatial and temporal learning data can be classified according to the user of the telephone. The different classifications can be the following: sex, age, social category of the user. Depending on the distribution of users in these different classifications and by comparison with other data known elsewhere, the spatial and temporal learning data or the learning trajectories can be corrected to correct the errors that would be induced by the raw data.In other words, by assigning a first adjustment coefficient, we can mathematically correct the acquired statistical data to better represent the real population.

[0112] According to an advantageous embodiment of the invention, in step c1), it is possible to group together different determined learning trajectories into clusters of learning trajectories. The grouping into clusters makes it possible to limit the number of iterations of step c2) and therefore to reduce the calculation time and the computer memory required. Indeed, by grouping the learning trajectories into clusters, it is possible to carry out step c2) only for one trajectory of each cluster.

[0113] Preferably, the learning trajectories can be grouped using an agglomerative clustering method based on a spatio-temporal distance, said spatio-temporal distance being a weighted sum of a spatial distance between two learning trajectories and a temporal distance depending on the difference in duration between the two learning trajectories, said spatial distance and the temporal distance being determined from the spatial and temporal learning data, the clusters differentiating the speeds and the trajectories. taking into account spatial and temporal data makes it possible to form, in a single step, groupings differentiating journeys of different speeds and different paths. In other words, each grouping includes trajectories taking similar paths at similar speeds, which makes it possible in particular to discriminate a bicycle journey from a car journey in congested traffic which would take the same time, but which are not carried out at the same pace (the car will go quickly in some places and very slowly in others, while the bicycle will be much more regular).

[0114] An agglomerative clustering method is a hierarchical clustering method that merges clusters in order of their degree of similarity until a predetermined number of groupings is reached, denoted here N^t^.

[0115] According to one embodiment, the spatiotemporal distance can be determined using the following formula:

[0116] dspatj&np — A-df with d^paîdei-np the spatiotemporal distance, dt the distance temporal, To a weighting scalar, which underlines the importance of the travel time (the displacement corresponding to a considered trajectory).

[0117] The hyperparameters to be set for the agglomerative clustering method can be the number of clusters required Ne}us(fi(,o and the weighting which allows to give more or less importance to the travel time in the clustering. The choice of the parameter À can depend on the type of the grouped information desired (clustering mainly temporal, spatial, or spatio-temporal).

[0118] As a non-limiting example, we can choose weighting 2 between 106 and 10 *. For each value of the weighting, we can then determine a silhouette score (as a reminder, calculated from the average intra-cluster distance and the average distance between the closest clusters) calculated for NciustMgg = 1 to 40 groupings. The combination of À and Nciust.agg giving the best silhouette score can then be chosen.

[0119] For the embodiment for which the spatial and temporal training data are preprocessed, the spatial distance can be determined using the following formula:

[0120] ; y.\-±VN pj Hr v Wy v with path i, A / le path j, d^t the spatial distance, N the number of points of the displacement, d!wversine a geodesic distance between two points, yj the coordinates of the k-th point of the displacement i, the coordinates of the k-th point of displacement j.

[0121] For the embodiment for which the spatial and temporal training data are preprocessed, the temporal distance can be determined by means of from the following formula:

[0122] x ( (ï v-? )2 with Xj the displacement i, the displacement j, the to^Xj, Xj) = .................... time distance, N the number of points of the displacement, the time of the last point of the displacement i, the time of the first point of the displacement i, "tj^ the time of the last point of the displacement], t ji the time of the first point of the displacement].

[0123] According to an advantageous variant of the invention, transport graphs can be constructed for each mode of transport, each transport graph comprising nodes, road portions connecting the different nodes and the average travel speeds on each road portion, each road portion of the transport graph representing the possible routes by the associated mode of transport, the superposition of the transport graphs forming the transport network, each strand of the transport network corresponding to at least one of said road portions of at least one of said transport graphs, then: i) from the acquired spatial and temporal learning data, it is possible to determine a succession of location-time pairs defined by locations corresponding to the positions of the antennas from the acquired spatial and temporal learning data and by the times corresponding to these locations and it is possible to form at least one sub-route connecting two successive location-time pairs, a succession of sub-routes determining a learning trajectory; ii) For each transport graph, the successive positions of the telephone on successive identified nodes of the transport graph can be determined from the location-time pairs, the successive identified nodes being positioned as close as possible to each location; iii) Then for each transport graph and for each sub-route, it is possible to determine, by shortest path optimizations, a predetermined number of possible paths making it possible to connect the different successive identified nodes by road sections, each possible path comprising the crossing nodes connecting all the road sections of the possible path, and it is possible to calculate the time of passage of each crossing node from the average speeds associated with each road section, each possible path being determined by the list of all the pairs of crossing nodes - times of passage of said possible path; iv) a correlation index can be determined between each of the determined possible paths and the learning trajectory, the correlation index being representative of the spatial proximity and / or the temporal proximity of each successive identified node with the locations of the learning trajectory; and (v) the mode of transport of each learning trajectory can be determined, by determination of the mode of transport which optimizes the correlation index of the learning trajectory, the path taken corresponding to the possible path optimizing the correlation index.

[0124] These steps make it possible to reconstruct the user's path (of the user's telephone) from acquired spatial and temporal learning data of very low resolution, which makes this method transposable to other data which could be measured with a higher spatial and / or temporal resolution.

[0125] Alternatively, the following method of identifying the mode of transport can be applied: - The mode of transport of each trajectory (or of each group of trajectories) is determined as a function of the speed of the trajectories (possibly of each group) and / or by interpolation of a mode of transport known to at least one trajectory belonging to the group (i.e. if the mode of transport of at least one trajectory belonging to the group is known, then this mode of transport is applied to all the trajectories of the group) and / or as a function of the trajectory of the group; and - Each trajectory of the group can preferably be assigned the group's mode of transport.

[0126] This method has the advantage of determining the mode of transport of a large number of trajectories in a simple manner, with reduced calculation time, and with limited computing resource requirements (memory and processor). In addition, due to the grouping by trajectory and speed, the determination of the mode of transport is more precise.

[0127] These steps can be implemented by computer means, in particular a computer or a server, comprising at least one processor and a computer memory.

[0128] The step of determining the mode of transport of each grouping may consist of: - Compare the speed of the trajectories of each group (which can be obtained by the distance of the trajectory and by the average duration of the trajectories of the group, or by a measurement of the speed simultaneously with the measurements of spatial and temporal data acquired) with a speed representative of the modes of transport: for example walking about 5 km / h, running about 10 km / h, bicycle about 20 km / h, motorized vehicle in town between 30 and 50 km / h, etc., and / or - Identify at least one trajectory of the grouping for which the mode of transport is known, for example by means of a step of acquiring the mode of transport, and / or - Identify the typology of the paths taken by the group's trajectory, for example: if it is a highway, the mode of transport is a motorized vehicle, if it is a path, the mode of transport is a soft mobility mode of transport.

[0129] In addition, these steps can make it possible to identify the road infrastructures to be put in place and / or the transport networks (soft mobility or public transport) to be developed. The method also makes it possible to monitor the evolution over time of users' choices of transport mode in a given geographical area, depending for example on the implementation of road infrastructures or transport networks.

[0130] In the present application, the notion of "succession" or "successive" indicates a succession in time (temporal). For example, two locations are successive if they are two locations which have been temporally identified one after the other.

[0131] [Fig.6] illustrates, in a schematic and non-limiting manner, an example of a step of identifying the mode of transport and the path of the method of determining the number of uses of different modes of transport on at least one strand of a transport network within a predetermined space according to the invention.

[0132] Transport graphs G_MDP are constructed for each mode of transport, each transport graph comprising nodes, road sections connecting the different nodes and the average travel speeds on each road section. Each road section of the transport graph G_MDP represents the possible routes by the associated mode of transport. The superposition of the transport graphs forms the transport network and each strand of the transport network corresponds to at least one of the road sections of at least one of the transport graphs.

[0133] From each learning trajectory Traj and transport graphs G_MDP, we determine Det the successive identified nodes NIS of each transport graph G_MDP, as nodes of the transport graph considered as the closest to the positions of the antennas to which the telephone is connected on the route (we can also consider only some of these positions instead of considering all of them), while being connected to each other (in other words, there are portions of road making it possible to connect the different successive identified nodes to each other).

[0134] From the successive identified nodes NIS, an optimization of the shortest paths Opt is carried out for each sub-route of each transport graph so as to identify a predetermined number of possible paths CP for each mode of transport.

[0135] Then we evaluate the correlation Corr between each possible path CP of each mode of transport and the learning trajectory Traj, composed of the sub-routes and we determines a correlation index Ind. By optimizing the correlation (via the correlation index Ind), we can determine Det-MDP the output data Ch which includes the mode of transport and the path taken on the transport network by the user for the learning trajectory concerned.

[0136] The steps described above may be implemented by computer means, for example a computer.

[0137] Preferably, a subgraph can be extracted from each transport graph, the subgraph being a part of the transport graph limited to a predetermined width around each sub-route determined in step i) and the subgraph can be used instead of the transport graph for each of steps ii) to v). As a result, the subgraph has a limited number of nodes and route portions, which makes it easier to determine the mode of transport and, on the other hand, to limit the possible paths determined in step iii). Thus, it is possible to quickly obtain a lot of information on the modes of transport chosen by the users according to their journey and also the changes in this information over time. As a result, it is easier to process the data, to increase the efficiency and speed of the method, and to limit the computer memory required for implementing the invention by computer.

[0138] Step d): creation of a model of the number of uses of each mode of transport on said at least one strand of the transport network

[0139] From the paths and modes of transport determined in step c2) on the learning trajectories of the different journeys between the origin zones and the destination zones, a model is produced of the number of uses (or the flow rate of users or uses, also called frequency, or a rate of use) of each mode of transport on at least one strand of the transport network concerned. This model links the trajectories to a number of uses (or the flow rate of users or uses, also called frequency, or a rate of use) of each mode of transport on the strand of the transport network concerned. In other words, the learning trajectories and the associated paths and modes of transport determined in step c2) are used to create the model of the number of uses of each mode of transport on the strand of the transport network concerned.The advantage of this model is that it can determine, very quickly and with limited computer memory and processor requirements, the impact of travel flow rates on the strands of the transport network, based on new data (for example, new trajectories, i.e. trajectories different from the learning trajectories). The new data can, for example, take into account planned changes to the transport network (changes or additions of lanes, changes to intersections, speed limits). They can also take into account data on other time slots or other . days than the learning trajectories. Thanks to the model, we can avoid applying steps b) and c) for new data, these steps being long and requiring a significant amount of memory and processors. Thus, we can test different configurations and quickly evaluate the impact in terms of user throughput or pollutant emissions (greenhouse gases, such as carbon dioxide for example, and / or pollutant particles for example) on a strand of the transport network. The new data correspond to the data matrix using in the following steps.

[0140] For example, to produce the model of the number of uses of each mode of transport on the relevant strand of the transport network, the following steps can be carried out: (dl) for each journey, a first number of learning trajectories linked to each mode of transport is identified. Thus, for each journey from an origin zone to a destination zone, the number of learning trajectories of each mode of transport is counted, this number defining the first number of each mode of transport;

[0141] The first number .f of learning trajectories of the route connecting the origin zone A to the destination zone B by the transport mode M can in particular be defined in the following manner:

[0142] / = W; • h -m

[0143] Where lexpr is the indicator which is equal to 1 if exPr is true and 0 otherwise. In this case, le-m is equal to 1 if the mode of transport is the mode of transport M and it is equal to 0 if it corresponds to another mode of transport different from the mode of transport M.

[0144] corresponds to the number of learning trajectories of the mode of transport concerned, possibly corrected by a correction coefficient.

[0145] corresponding to the set of trajectories connecting the original zone A to the zone of destination B.

[0146] d2) for each path, we identify a second number of trajectories learning trajectories linked to each mode of transport and passing through the relevant strand of the transport network. Thus, for each journey from an origin zone to a destination zone, the number of learning trajectories of each mode of transport passing through the relevant strand is counted, this number defining the second number of each mode of transport passing through the relevant strand.

[0147] The second number / BMb of learning trajectories of the route connecting the origin zone A to the destination zone B by the transport mode M and passing through the strand b; of the transport network can in particular be defined in the following manner: [DUS] f1 / ,^,

[0149] Where Uxpr is the indicator which is equal to 1 if exPr is true and 0 otherwise. In this case,: - 1£-=a / is equal to 1 if the mode of transport is the mode of transport M and it is equal to 0 if it corresponds to another transport mode different from the transport mode M; - is equal to 1 if the strand considered bt belongs to the path Bj identified for the learning trajectory (path Bj comprising a sequence of strands of the transport network) and it is equal to 0 if path Bj does not include strand Bj.

[0150] wj corresponds to the number of learning trajectories of the mode of transport concerned, possibly corrected by a correction coefficient as explained previously.

[0151] Qajs corresponding to the set of trajectories connecting the origin zone A to the destination zone B.

[0152] d3) then for each path, we determine the proportion of trajectories learning related to each mode of transport and passing through the relevant strand of the transport network, this proportion being, for each mode of transport, the ratio between the second number and the first number; this proportion is particularly interesting because it changes little over time. Thus, we can use this proportion which becomes an intrinsic characteristic of the distribution of the different modes of transport of each journey and of the passage on the relevant strand.

[0153] For example, the proportion ^^^.^of learning trajectories of the route connecting the origin zone A to the destination zone B by the transport mode M and passing through the strand bj of the transport network can be determined by:

[0154] ^a^m^ “ fABM

[0155] With f being the second number of learning trajectories of the route connecting the origin zone A to the destination zone B by the transport mode M and passing through the strand bt of the transport network

[0156] And f ^BM the first number of learning trajectories of the path connecting the area from origin A to destination area B by mode of transport M.

[0157] d4) we produce the model of the number of uses of each mode of transport on the relevant strand of the transport network from the following equation: f = VV / y ? with f the number of uses of the mode of JM,bt ^AéT^B^AéT^A^M^ AB.MJ Mb, predetermined transport M passing through said at least one strand bi of the transport network ^ABAfp, the proportion calculated in step d3) for each journey from the origin zone A to the destination zone B of the predetermined transport mode M passing through said at least one strand bi of the transport network ^bm 'C number of trips (from the data matrix) of the journey from the origin zone A to the destination zone B for the predetermined transport mode M. F being the space of the transport network considered which includes all the origin zones and all the destination zones.

[0158] Therefore, the number of uses f of the predetermined mode of transport M 7 M,bt passing through said at least one strand bi of the transport network is the sum over all the journeys going from the different origin zones A to the different destination zones B, of the product of the proportion concerned and the number of trips of the data matrix of the journey leaving from the origin zone A to the destination zone B for the predetermined transport mode M.

[0159] [Fig.2] illustrates, in a schematic and non-limiting manner, an exemplary embodiment of the model of the number of uses of each mode of transport on the considered strand of the transport network of the method for determining the number of uses of different modes of transport on the considered strand of a transport network within a predetermined space according to the invention.

[0160] In this example, we construct Real the model Mod of the number of uses of each mode of transport on a strand of the transport network from the output data Ch, which includes, for each learning trajectory, the path taken on the transport network (as a succession of strands on the transport network) and the associated mode of transport.

[0161] To do this, from the output data Ch, for each path, we identify INI a first number N1 and we identify IN2 a second number N2.

[0162] The first number NI corresponds for each journey, to the number of learning trajectories linked to each mode of transport.

[0163] The second number N2 corresponds for each journey, to the number of learning trajectories linked to each mode of transport and passing through the considered strand of the transport network.

[0164] From the first and second numbers for each journey, for each journey, we determine I_ratio the proportion ratio of learning trajectories linked to each mode of transport and passing through the considered strand of the transport network, this proportion ratio being, for each mode of transport and each journey, the ratio between the second number N2 and the first number NI.

[0165] We can then establish Eq the Mod model from the proportion ratio and new data (called data matrix in step e)).

[0166] Step e): acquisition of a data matrix and application of the data matrix to the model of the number of uses for each mode of transport on the strand considered in the transport network.

[0167] During this step, a data matrix is ​​acquired. This data matrix can give, for each journey from an origin zone to a destination zone, the number of journeys made (over a predetermined duration for example). This can be the total number of journeys made (all modes of transport combined) or the number of journeys made for each type of mode of transport. This data matrix can be obtained in different ways: for example by using CDR, NSD data, by measuring vehicles on sections of road, by surveys, etc. This data matrix can in particular be determined at a time distinct from the data used for the learning trajectories or be derived from a projection taking into account a modification on the transport network.

[0168] The data matrix may for example be determined from second trajectories which are new trajectories, distinct from the learning trajectories. These second trajectories may correspond to modifications envisaged or made to the transport network and / or trajectories acquired at times and / or days different from the learning trajectories.

[0169] The data matrix (which can be determined from the second trajectories for example) is then applied to the model of the number of uses (or the flow rate of users or uses, also called frequency, or a rate of uses) for each mode of transport on the considered strand of the transport network to determine the number of uses (or the flow rate of users or uses, also called frequency, or a rate of uses) of each mode of transport on this strand of the transport network from the data matrix (and / or any second trajectories).

[0170] Advantageously, it is possible to acquire second spatial and temporal data of connection of telephones to the mobile telephone network, the second spatial and temporal data comprising the positions of the antennas of the mobile telephone network to which each telephone has connected and the times at which these connections of each telephone to the antennas took place.

[0171] The second spatial and temporal data may be NSD data and preferably they may be CDR data, as defined previously.

[0172] It is then possible, from the second spatial and temporal data, to determine second trajectories and from these second trajectories, it is possible to construct a data matrix which defines, for each journey from an origin zone to a destination zone, the number of journeys made for each type of mode of transport (over a predetermined duration for example). The data matrix is ​​thus formed.

[0173] Preferably, a second correction coefficient can be assigned to the second trajectories based on the users of the telephones for which the second spatial and temporal data were acquired. For example, the second spatial and temporal data can be classified based on the user of the telephone. The different classifications can be the following: sex, age, social category of the user. Depending on the distribution of users in these different classes and by comparison with other data known elsewhere, the second spatial and temporal data or the second trajectories can be corrected to correct the errors that would be induced by the raw data. In other words, by assigning a second correction coefficient, the acquired statistical data can be mathematically corrected to better represent the real population.

[0174] [Fig.4] illustrates, in a schematic and non-limiting manner, a third embodiment of the method for determining the number of uses of different modes of transport on at least one strand of a transport network within a predetermined space according to the invention.

[0175] In this figure, the elements and references identical to [Fig.l] correspond to the same elements and references as [Fig.l] and are therefore not re-detailed.

[0176] In this embodiment, to determine For2 the data matrix Mat, Acq2 is acquired from the second spatial and temporal data DSP2 from the connection data of telephones Tel of the mobile telephone network Res.

[0177] These second spatial and temporal data DSP2 are used to determine Detl the second trajectories Traj2, from which the data matrix Mat is determined For2.

[0178] Before the step of determining Detl of the second trajectories Traj2, a pre-processing step Pre2 and / or a filtering step Fil2 of the second spatial and temporal data DSP2 can be carried out. These pre-processing steps Pre2 and filtering steps Fil2 can be identical to the pre-processing and filtering steps (respectively called Pre and Fil in [Fig.3]) applied to the spatial and temporal learning data DSP.

[0179] Preferably, the second spatial and temporal data DSP2 are of the same type as the spatial and temporal training data DSP (for example, they are NSD data and preferably, they are CDR data).

[0180] The pre-processing step Pre2 can be carried out before the filtering step Fil2 as illustrated in [Fig.4], or in the reverse order.

[0181] Of course, all the steps described can be carried out on several strands of the transport network, or even on all the strands of the transport network.

[0182] [Fig.5] illustrates, in a schematic and non-limiting manner, a fourth mode of implementation of the method for determining the number of uses of different modes of transport on at least one strand of a transport network within a predetermined space according to the invention.

[0183] In this figure, the elements and references identical to [Fig.l] correspond to the same elements and references as [Fig.l] and are therefore not re-detailed.

[0184] In this embodiment, to determine the learning trajectories Traj, spatial and temporal learning data DSP are acquired Acq from the connection data of telephones Tel of the mobile telephone network Res.

[0185] These spatial and temporal learning data DSP are used to determine For the intermediate learning trajectories Traj_int which are the set of trajectories corresponding to the spatial and temporal learning data DSP (one intermediate learning trajectory for each telephone). Then the intermediate learning trajectories Traj_int are grouped into clusters which then form the learning trajectories Traj which are used in the step of identifying MDT the mode of transport and the path traveled on the transport network. This makes it possible to limit the number of learning trajectories to which the mode of transport and the path are identified MDT and therefore also to limit the number of learning trajectories for the step of building Real the model Mod.

[0186] In this embodiment, to determine the data matrix Mat, Acq2 is acquired from the second spatial and temporal data DSP2 from the connection data of telephones Tel of the mobile telephone network Res.

[0187] These second spatial and temporal data DSP2 are used to determine Detlb the second intermediate trajectories Traj2_int which are the set of trajectories corresponding to the second spatial and temporal data DSP2 (one intermediate trajectory for each telephone). Then we group reg2 the second intermediate trajectories Traj2_int into clusters which then form the second trajectories Traj2, used to determine For2 the data matrix Mat. The data matrix Mat is then used in the application step App of the model Mod. This makes it possible to limit the number of trajectories to which we apply App the model Mod.

[0188] During the regrouping steps governed and reg2, the output trajectories (respectively the learning trajectories Traj and the second trajectories Traj2), which are each a cluster of trajectories, are assigned a number of trajectories corresponding to the number of intermediate trajectories grouped in the cluster concerned, so as to be able to count the number of similar trajectories of each cluster, and thus to determine the number of uses of each mode of transport of the strand concerned of the transport network.

[0189] Before the step Detlb of determining the second intermediate trajectories Traj2_int, a pre-processing step and / or a filtering step of the second spatial and temporal data DSP2 can be carried out. These pre-processing and filtering steps of the second spatial and temporal data DSP2 can be identical to the pre-processing and filtering steps of the second spatial and temporal data DSP2 (respectively called Pre2 and Fil2 in [Fig.4]) and can be identical to the pre-processing and filtering steps applied to the spatial and temporal learning data (respectively called Pre and Fil in [Fig.3]).

[0190] Preferably, the second spatial and temporal data DSP2 are of the same type as the spatial and temporal training data DSP (for example, they are NSD data and preferably, they are CDR data).

[0191] The pre-processing step can be performed before the filtering step as illustrated in [Fig.4], or in the reverse order.

[0192] Of course, all the steps described can be carried out on several strands of the transport network, or even on all the strands of the transport network.

[0193] Of course, the different embodiments of figures 1, 3, 4 and 5 can be combined with each other (two by two, three by three or all together) without departing from the scope of the invention.

[0194] According to an advantageous embodiment of the invention, the second trajectories can be grouped into clusters of second trajectories. The grouping into clusters makes it possible to limit the number of iterations and therefore to reduce the calculation time and the computer memory required. Indeed, by grouping the second trajectories into clusters, step e) can be carried out only for a second trajectory of each cluster.

[0195] Preferably, the second trajectories can be grouped by means of an agglomerative grouping method based on a spatio-temporal distance, said spatio-temporal distance being a weighted sum of a spatial distance between two second trajectories and a temporal distance depending on the difference in duration between the two second trajectories, said spatial distance and the temporal distance being determined from the second spatial and temporal data, the groupings differentiating the speeds and the trajectories. Taking into account spatial and temporal data makes it possible to form, in a single step, groupings differentiating paths of different speeds and different paths.In other words, each grouping includes second trajectories taking similar paths at similar speeds, which makes it possible in particular to discriminate between a bicycle journey and a car journey in congested traffic which would take the same amount of time, but which are not carried out at the same pace (the car will go quickly in some places and very slowly in others, whereas the bicycle will be much slower). more regular).

[0196] When groupings have already been carried out on the learning trajectories, it is also possible to use the learning trajectories resulting from these groupings, i.e. to reuse the clusters of learning trajectories, for the second trajectories. Each of the second trajectories can be associated with the closest cluster among the learning trajectories. Thus, a path and the mode of transport can be associated with each second trajectory.

[0197] According to a configuration of the invention, the number of uses (or the flow rate, or the rate or the frequency of uses or the users) of each mode of transport passing through at least one strand of the transport network, preferably through at least one group of strands of the transport network and more preferably through all the strands of the transport network, can be displayed on a map representing the transport network. This display allows easy reading of the results. This display can take the form of a note or a color code or a thickness of representation of each strand on the map. This display can be carried out on board the vehicle: on the dashboard, on a stand-alone portable device, such as a geolocation device (GPS type), a mobile phone (smartphone type) or a computer.It is also possible to display the number of uses (or flow, or rate or frequency of uses or users) of each mode of transport passing through at least one strand of the transport network on a website. In addition, the number of uses (or flow, or rate or frequency of uses or users) of each mode of transport passing through at least one strand of the transport network can be shared with public authorities (e.g. road manager) and public works companies. Thus, public authorities and public works companies can determine the roads with a high user flow and the associated mode of transport, and the adjustments to be made to the transport network to limit congestion or limit the polluting emissions emitted (e.g. creation of new lanes, modification of signage, etc.).

[0198] The invention relates to a method for determining pollutant emissions on at least one strand of a transport network within a predetermined space. For this method, the method for determining the number of uses (or the rate or flow rate or frequency of uses or users) of different modes of transport is implemented on at least one strand of a transport network within a predetermined space as described above and the pollutant emissions due to each mode of transport of each learning trajectory and / or of the data matrix passing through the strand concerned are determined for the at least one strand of the transport network. This method can in particular be used to modify transport network infrastructures (adding lanes, building specific lanes for modes of particular transport, bicycle or scooter for example), to modify the speed limits of certain (at least one) strands of the transport network and / or to prohibit the circulation of certain vehicles on certain (at least one) strands of the transport network, by evaluating the impact of these modifications on the quantity of polluting emissions emitted on the strands of the transport network and consequently on air quality.

[0199] Advantageously, the pollutant emissions of each mode of transport can be determined by multiplying a pollutant emissions value of each mode of transport on the relevant strand of the transport network and the number of uses of each associated mode of transport on said relevant strand of the transport network. Thus, an evaluation of the pollutant emissions can be quickly established as a function of the different modes of transport of the relevant strand.

[0200] Alternatively or additionally, when the modes of transport are linked to vehicles, the quantity of polluting emissions emitted by each vehicle of each mode of transport on a strand can be determined by the method described in patent application FR3122011 A1.

[0201] Alternatively or additionally, the following method may be applied:

[0202] Furthermore, the invention relates to a method for determining a quantity of pollutants emitted by a plurality of paths. For this method, the following steps are implemented: - we apply a pollutant emissions model, which links the speed and the trajectory to a quantity of at least one pollutant emitted, thus we obtain a quantity of pollutant emissions per trajectory; and - The quantity of at least one pollutant emitted by said plurality of trajectories is determined.

[0203] The model of pollutant emissions can in particular be written in the form:

[0204] q = trai \ with <2 z a quantity of emissions of the trajectory considered, vw° a speed of the vehicles for the trajectory considered, / raj the trajectory considered, f a function corresponding to the model.

[0205] The function f can be obtained from a vehicle dynamic model, or by machine learning, or by any analogous means.

[0206] Then, we can determine a quantity of pollutants by the set of trajectories using a formula of the type: 102071, chicken pot^ro l

[0208] With the total quantity of pollutants emitted, Nc]ust^s the number of groupings (if groupings are used) or trajectories, the weighting of the grouping or trajectory 1, Qpoigro / the quantity of pollutants emitted for a trajectory or for grouping 1.

[0209] The weighting of the grouping œi is advantageously proportional to the number of trajectories within the grouping.

[0210] According to an advantageous implementation of the method of the invention, a fleet of vehicles can be applied to determine the pollutant emissions, said fleet of vehicles identifying a distribution of different types of vehicles and a value of pollutant emissions according to the different types of vehicles. The fleet of vehicles can be the current fleet of vehicles crossing the transport network considered. It can also be defined according to the recording histories and / or prior knowledge of the fleet of vehicles in the area considered (i.e. the transport network). Thus, the predefined fleet is a distribution in number or percentage of vehicles, of each predetermined vehicle, circulating on the portion of the transport network.Within the vehicle fleet, the different vehicles are categorized, the vehicle category can include in particular a European standard of pollutant emissions, a cylinder capacity, a type of engine (petrol, diesel, electric, etc.), and an aftertreatment technology. This breakdown of the vehicle fleet can be carried out for private vehicles, heavy goods vehicles, light commercial vehicles, two-wheelers, etc. By taking into account the vehicle fleet, the determination of pollutant emissions is more representative of real conditions or future conditions. Indeed, the vehicle fleet can be a projection into the future of the distribution of the different categories of vehicles, with the aim of testing different configurations to limit pollution (pollutant emissions) in certain areas.

[0211] Thus, the application of a pollutant emissions model can take into account the fleet of vehicles used for the transport network considered.

[0212] Preferably, the determined pollutant emissions can be displayed on a map representing the transport network. This display allows easy reading of the results. This display can take the form of a note or a color code or a thickness of representation of each strand on the map. This display can be carried out on board the vehicle: on the dashboard, on a stand-alone portable device, such as a geolocation device (GPS type), a mobile phone (smart phone type) or a computer. It is also possible to display the number of uses (or the flow rate, or the rate or frequency of uses or users) of each mode of transport passing through at least one strand of the transport network on a website. In addition, the pollutant emissions of at least one strand of the transport network can be shared with public authorities (for example, road manager) and public works companies.In this way, public authorities and public works companies can determine the sections of the transport network with a high level of pollutant emissions, and the adjustments to be made to the network. transport to limit polluting emissions (for example, creation of new routes, modification of signage, etc.).

[0213] Furthermore, the invention relates to a method for managing infrastructure of a transport network within a predetermined space. For this method, the following steps can be implemented: a. The number of uses and / or the pollutant emissions of each mode of transport for at least one strand of the transport network are determined by means of the method for determining the number of uses of each mode of transport on at least one strand of the transport network within the predetermined space or the method for determining the pollutant emissions according to any one of the variants or combinations of variants described above; and b. At least one transport network infrastructure is modified based on the number of uses of each mode of transport or pollutant emissions, for example an infrastructure for which the number of uses is higher than a predetermined threshold.

[0214] Thus, a transport network can be managed to limit or even avoid pollution peaks, traffic jams and accident risks.

[0215] According to one embodiment, the modification of the infrastructure can be chosen in particular from the addition of signage (speed limit, traffic light, give way, stop, etc.), the construction of a new lane, passage of a one-way strand, construction of a new road, etc. Examples

[0216] The method according to the invention was tested by the following example.

[0217] CDR data were used as training spatial and temporal data and as second spatial and temporal data, at the scale of a large metropolis.

[0218] These data were used at two distinct periods: - a first period called “normal”, between February 22, 2020, and March 1, 2020 and; - a second period called “covid”, between April 1 and 7, 2020.

[0219] These two periods were deliberately chosen to have a circulation period normal before the confinement due to the Covid 19 pandemic and another period with very different traffic due to the confinement of the population during the Covidl9 pandemic.

[0220] During the normal period, 326,208,648 telephone records were acquired; during the covid period, 128,406,026 records were acquired.

[0221] The records each correspond to spatial and temporal data of the CDR type.

[0222] From this data, trajectories could be determined between origin zones and destination zones.

[0223] The normal period is used for determining learning trajectories and the learning model and the covid period for determining the second trajectories.

[0224] Given the high number of data, the spatial and temporal training data (normal period) as well as the second spatial and temporal data (covid period) were grouped into a cluster to limit computing resources and calculation time.

[0225] Pollutant emissions were determined for a fleet of vehicles comprising 18% heavy goods vehicles, 6% heavy goods vehicles, 1% two-wheelers and 75% private vehicles. The proportions of motorization of the different vehicles by standard are specified in the tables below. Distribution of passenger vehicle engines Standard Share (%) Eurol Diesel 0.6 Eurol Gasoline 0.3 Euro2 Diesel 1.3 Euro2 Gasoline 0.8 Euro3 Diesel 5.1 Euro3 Gasoline 2 Euro4 Diesel 21.5 Euro4 Gasoline 6.5 Euro5 Diesel 22 Euro5 Gasoline 9.2 Euroô Diesel 13.6 Euroô Gasoline 16.1 Distribution of light commercial vehicle engines Standard Share (%)

[0226]

[0227]

[0228] Euro3 P 1.7 Euro4 P 2.8 Euro5 P 4.2 Euro3 M 10.1 Euro4 M 17.2 Euro5 M 25.6 Euro6 M 37.8 Distribution of heavy goods vehicle engines Standard Share (%) HGV 1 5.3 HGV 2 1 HGV 3 10 HGV 4 1.3 HGV 5 12.6 HGV 6 6.6 HGV 7 28.7 HGV 8 34.5 [Fig.7] illustrates the comparison of pollutant emissions emitted on each strand of the transport network in the metropolis concerned for the normal period on the right and for the covid period on the left. On the map representing the transport network of this metropolis (which thus defines the predetermined space), the gray levels represent the variations in pollutant emissions on each strand: the darker the gray level, the higher the pollutant emissions (the poorer the air quality). Conversely, the lighter the gray level, the lower the pollutant emissions (the better the air quality). Thanks to the method of the invention, a very significant difference in the pollutant emissions emitted on each strand of the transport network can be observed, with pollutant emissions being much lower during the covid period on the left than during the normal period on the right. In addition, thanks to the model, it was not necessary to reproduce steps b) to d) which require a lot of memory and computer processors. The results for the covid period were obtained from a matrix of data and the model obtained from the learning trajectories, very quickly.

Claims

1. Claims Method for determining the number of uses of different modes of transport on at least one strand of a transport network within a predetermined space, by means of telephones (Tel) and a mobile telephone network (Res) to which said telephones (Tel) can connect, and by means of at least one transport network (RT) comprising strands, characterized in that at least the following steps are carried out by computer means, such as a computer: a) spatial and temporal learning data (DSP) of connection of telephones (Tel) to said mobile telephone network (Res) are acquired (Acq), the spatial and temporal learning data (DSP) comprising the positions of the antennas of the mobile telephone network (Res) to which each telephone (Tel) has connected and the times at which these connections of each telephone (Tel) to the antennas took place,preferably the spatial and temporal learning data (DSP) being CDR or NSD data b) the predetermined space is discretized into zones;, c) for each path defined by an origin zone (ZD) and a destination zone (ZA), each origin zone (ZD) and each destination zone being among said zones of the predetermined discretized space, cl) learning trajectories (Traj) of said path are determined (For), from the acquired spatial and temporal learning data (DSP), each learning trajectory (Traj) corresponding to the succession of positions of the antennas to which one of said telephones (Tel) has connected and the times of these connections; c2) we determine (MDT), for each learning trajectory (Traj) determined, the path taken on the transport network and the associated mode of transport, the path taken being a succession of strands of the transport network (RT) at times of passage; d) from the paths and modes of transport determined in step c2), a model (Mod) is produced (Real) of the number of uses of each mode of transport on said at least one strand of the transport network linking the trajectories to a number of uses (Nb_mdt) of each mode of transport on said at least one strand of the transport network (RT); e) a data matrix (Mat) is acquired and said data matrix (Mat) is applied to said model (Mod) of the number of uses for each mode of transport on said at least one strand of the transport network to determine the number of uses (Nb_mdt) of each mode of transport on said at least one strand of the transport network (RT) from said data matrix (Mat).

2. Method according to claim 1, wherein in step d), to produce (Real) the model (mod) of the number of uses (Nb_mdt) of each mode of transport on said at least one strand of the transport network, the following substeps are carried out: dl) for each journey, a first number (NI) of learning trajectories linked to each mode of transport is identified; d2) for each journey, a second number (N2) of learning trajectories linked to each mode of transport and passing through said at least one strand of the transport network (RT) is identified; d3) then for each journey, the proportion (ratio) of learning trajectories (Traj) linked to each mode of transport and passing through said at least one strand of the transport network (RT) is determined (I_ratio), this proportion (ratio) being, for each mode of transport, the ratio between the second number (N2) and the first number (NI);d4) the model (Mod) of the number of uses of each mode of transport on said at least one strand of the transport network is produced (Eq) from the following equation: f — V yf with / ' the number of uses J Mit AJ!MJ of the predetermined mode of transport M passing through said at least one strand bi of the transport network the proportion calculated in step d3) for each journey leaving from the origin zone A to the destination zone B of the predetermined mode of transport M passing through said at least one strand bi of the transport network fM 'C number of trajectories of the journey leaving from the origin zone A to the destination zone B for the predetermined mode of transport F being the space of the transport network (RT) considered.;

3. Method according to one of the preceding claims, in which pre-processing (Pre) and / or filtering (Fil) of the spatial and temporal learning data (DSP) is carried out.

4. Method according to one of the preceding claims, in which a first rectification coefficient is assigned to said learning trajectories (Traj) as a function of the users of the telephones (Tel). for which the spatial and temporal training data (DSP) were acquired in step a).

5. Method according to one of the preceding claims, in which second spatial and temporal data (DSP2) of connection of telephones (Tel) to said mobile telephone network (Res) are acquired (Acq2), the second spatial and temporal data (DSP) comprising the positions of the antennas of the mobile telephone network (Res) to which each telephone (Tel) has connected and the times at which these connections of each telephone (Tel) to the antennas took place, and second trajectories (Traj2) are determined (Detl) so as to form (For2) said data matrix (Mat) acquired from the second spatial and temporal data (DSP2) and preferably a second rectification coefficient is assigned to said second trajectories (Traj2) as a function of the users of the telephones for which the second spatial and temporal data (DSP2) have been acquired.

6. Method according to claim 5, in which the second trajectories are grouped (reg2) into clusters of second trajectories (Traj2), preferably by means of an agglomerative grouping method based on a spatio-temporal distance, said spatio-temporal distance being a weighted sum of a spatial distance between two second trajectories and a temporal distance depending on the difference in duration between the two second trajectories, said spatial distance and the temporal distance being determined from the second spatial and temporal data (DSP2), the groupings differentiating the speeds and the trajectories.

7. Method according to one of the preceding claims, in which in step cl), different learning trajectories (Traj_int) determined are grouped (regi) into clusters of learning trajectories (Traj).

8. Method according to claim 7, in which the learning trajectories (Traj_int) are grouped by means of an agglomerative grouping method based on a spatio-temporal distance, said spatio-temporal distance being a weighted sum of a spatial distance between two learning trajectories (Traj_int) and a temporal distance depending on the difference in duration between the two learning trajectories (Traj_int), said spatial distance and the temporal distance being determined from the spatial and temporal learning data (DSP), the groupings differentiating the speeds and trajectories.

9. Method according to one of the preceding claims, in which, in steps cl) and c2), at least the following sub-steps are carried out: - transport graphs (G_MDP) are constructed for each mode of transport, each transport graph (G_MDP) comprising nodes, road portions connecting the different nodes and the average travel speeds on each road portion, each road portion of the transport graph (G_MDP) representing the possible routes by the associated mode of transport, the superposition of the transport graphs (G_MDP) forming the transport network (RT), each strand of the transport network (RT) corresponding to one of said road portions of at least one of said transport graphs (G_MDP), in which at least the following steps are implemented: i) from the acquired spatial and temporal learning data (DSP), a succession of location-time pairs defined by locations corresponding to said positions of the antennas from the acquired spatial and temporal learning data (DSP) and by the times corresponding to these locations is determined and at least one sub-route connecting two successive location-time pairs is formed, a succession of sub-routes determining a learning trajectory (Traj); ii) For each transport graph (G_MDP), we determine (Det) the successive positions of the telephone on successive identified nodes (NIS) of the transport graph (G_MDP) from the location-time pairs, the successive identified nodes (NIS) being positioned as close as possible to each location; iii) Then for each transport graph (G_MDP) and for each sub-route, we determine (Opt), by shortest path optimizations, a predetermined number of possible paths (CP) making it possible to connect the different successive identified nodes (NIS) by road sections, each possible path (CP) comprising the crossing nodes connecting all the road sections of the possible path (CP), and we calculate the time of passage of each crossing node from the average speeds associated with each road section, each possible path (CP) being determined by the list of all the pairs crossing nodes - times of passage of said possible path (CP); iv) we determine (Corr) a correlation index (Ind) between each of the possible paths (CP) determined and the learning trajectory, the correlation index (Ind) being representative of the spatial proximity and / or the temporal proximity of each successive identified node (NIS) with the locations of the learning trajectory (Traj); and v) the mode of transport (MDP) of each learning trajectory is determined (Det_MDP), by determining the mode of transport which optimizes the correlation index (Ind) of the learning trajectory, and the path traveled corresponding to the possible path optimizing the correlation index.

10. The method of claim 9, wherein a subgraph is extracted from each transport graph, the subgraph being a portion of the transport graph (G_MDP) limited to a predetermined width around each sub-route determined in step i) and the subgraph is used instead of the transport graph (G_MDP) for each of steps ii) to v).

11. Method according to one of the preceding claims, for which the number of uses (Nb_mdt) of each mode of transport passing through at least one strand of the transport network (RT), preferably through at least one group of strands of the transport network (RT) and more preferably through all the strands of the transport network (RT), is displayed on a map representing the transport network (RT).

12. Method for determining pollutant emissions (Pol) on at least one strand of the transport network (RT) within a predetermined space, characterized in that the method according to one of the preceding claims is implemented and in that the pollutant emissions (Pol) due to each mode of transport of each learning trajectory (Traj) and / or of the data matrix (Mat) passing through said strand are determined (Cale).

13. Method according to claim 12, in which the polluting emissions (Pol) of each mode of transport are determined (Cale) by multiplying a value of polluting emissions of each mode of transport on said at least one strand of the transport network (RT) and the number of uses (Nb_mdt) of each associated mode of transport on said at least one strand of the transport network (RT).

14. Method according to one of claims 12 or 13, in which the pollutant emissions (Pol) determined are displayed on a map representing the transport network (RT).

15. Method according to one of claims 12 to 14, in which a fleet of vehicles is applied to determine (Cale) the pollutant emissions (Pol), said fleet of vehicles identifying a distribution of different vehicle types and a pollutant emissions value based on the different vehicle types.

16. Method for managing the infrastructure of a transport network within a predetermined space, in which at least the following steps are implemented: 1) The number of uses and / or the pollutant emissions of each mode of transport for at least one strand of the transport network are determined by means of the method for determining the number of uses of each mode of transport on at least one strand of the transport network within the predetermined space according to one of claims 1 to 11 or the method for determining the pollutant emissions according to one of claims 12 to 15; and 2) At least one infrastructure of the transport network is modified according to the number of uses of each mode of transport or pollutant emissions, preferably an infrastructure for which the number of uses is greater than a predetermined threshold.

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